Green Nanofabrication Opportunities in the Semiconductor Industry: A Life Cycle Perspective

Abstract

The turn of the 21st century heralded in the semiconductor age alongside the Anthropocene epoch, characterised by the ever-increasing human impact on the environment. The ecological consequences of semiconductor chip manufacturing are the most predominant within the electronics industry. This is due to current reliance upon large amounts of solvents, acids and gases that have numerous toxicological impacts. Management and assessment of hazardous chemicals is complicated by trade secrets and continual rapid change in the electronic manufacturing process. Of the many subprocesses involved in chip manufacturing, lithographic processes are of particular concern. Current developments in bottom-up lithography, such as directed self-assembly (DSA) of block copolymers (BCPs), are being considered as a next-generation technology for semiconductor chip production. These nanofabrication techniques present a novel opportunity for improving the sustainability of lithography by reducing the number of processing steps, energy and chemical waste products involved. At present, to the extent of our knowledge, there is no published life cycle assessment (LCA) evaluating the environmental impact of new bottom-up lithography versus conventional lithographic techniques. Quantification of this impact is central to verifying whether these new nanofabrication routes can replace conventional deposition techniques in industry as a more environmentally friendly option.

A Content Review of Life Cycle Assessment of Nanomaterials: Current Practices, Challenges, and Future Prospects

“…The goal and scope definition addresses the strategies used to meet the assumptions made regarding NMs pathways. The FU is the quantified performance of NMs used as a reference unit in the LCA study, where a fixed value is set, and the output results on the environmental impacts from the impact categories reflect on this selected FU, where the margins of error and explicitly specified data uncertainties should be incorporated [90]. Table 1 shows that more than half of the reviewed studies used a simplified FU, relating to the weight of the material (e.g., 1 kg of a polymer nanocomposite).…”

“…However, to perform a comparative LCA (e.g., comparing NMs with conventional materials), the FU should not be solely based on weight as it is not functionally comparable between the two products [11,91,92]. In fact, the FU should reflect the significance in evaluating the function and performance of such systems, where all processes, as well as their inputs and outputs, are linearly scaled [6,90]. A proper and adequate functional unit that considers all the additional functionalities of NMs must be considered in future studies in this field, to provide more realistic and fair potential benefits of nanomaterials in advanced technologies.…”

“…Inventory database such as Ecoinvent is the most widely used by the researchers in the previous studies. The inputs in the inventory include raw materials, energy (renewable and non-renewable), and water, while outputs are the products and co-products-emissions to air, water, and soil [90]. Other databases such as BEES and ILCD are also used, depending on the study's scopes and objectives that provide multicriteria fate modelling (such as USEtox, ReCiPe, and TRACI models) for evaluating the environmental impacts, fate, and exposure of certain products in the environment.…”

“…Predicting the fate and behavior of NMs in the environment requires a specific understanding of the potential sources, distribution of NMs once it was released to the environment, transformation of NMs in the environment, and the persistence or adapt-ability of NMs in the environment [14]. There are still large uncertainties in each stage of modelling NMs transport by using existing models that cannot be quantified and are inconclusive [90].…”

AI ethics: A framework for measuring embodied carbon in AI systems

“…The significant majority of the discussions focus rather on its positive effects on measuring or improving humanities response to climate change [4,5]. As a result, the impact that AI has from a climate justice perspective are also not widely discussed the fact that the beneficiaries of AI are mainly based in the so-called developed nations while the lower GDP countries will increasingly face the burdens of dealing with the environmental impact of AI; initial work in this space has been done related to e.g., impacts of the semiconductor industry [6][7][8][9], but there is no assessment of the justice aspects of destroying the environment in Malaysia for the benefit of users in the USA, Europe, Oceania and elsewhere in Asia.…”